Nanosecond Excimer Laser Ablation (W,Ti)C-Co Cemented Carbide

Yang, Jiao; Miao, Xuan; Zc, Wang; Wang, Xi Bing

doi:10.4028/www.scientific.net/amr.583.333

Cited by 2 publications

(2 citation statements)

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“…During the forward pass of the LSTM, the gates are computed based on the current input, previous output, and previous Ct. The equations governing the computation of the gates are as follows [21][22][23]:…”

Section: Cellmentioning

confidence: 99%

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Enhancing Performance of Movie Recommendations Using LSTM With Meta Path Analysis

Ali,

Muhammad,

Al-Shamayleh

et al. 2023

IEEE Access

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Movie recommendation algorithms play an important role in assisting consumers in identifying films that match their own likes. Deep Learning, particularly Long Short-Term Memory (LSTM) networks, have showed substantial promise in collecting sequential patterns to improve movie recommendations among the different techniques used for this purpose. Long Short-Term Memory-Inter Intra Metapath Aggregation (LSTM-IIMA) in movie recommendation systems is proposed in this study, with a specific focus on incorporating intra and inter metapath analysis. The intra metapath analysis investigates interactions within a single metapath, whereas the inter metapath analysis investigates links between numerous metapaths. Intra and inter metapath analyses are used in the LSTM-based movie recommendation system LSTM-IIMA to capitalise on these rich linkages. Each metapath sequence records the dependencies of a user's interactions with films and other things. The LSTM architecture has been modified to handle these metapath sequences, processing them to record temporal dependencies and entity interactions. To optimize the parameters and minimize prediction errors, the model is trained using supervised learning techniques. To measure the quality and usefulness of the recommendations, the LSTM-IIMA evaluation incorporates metrics such as precision, recall, ablation analysis, time efficiency and Area Under the Curve (AUC). The performance of the system is compared to that of alternative recommendation techniques HAN and MAGNN. Overall, incorporating intra and inter metapath analysis into the LSTM-IIMA improves its ability to capture complex linkages and dependencies between movies, users, and other things.

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Section: Cellmentioning

confidence: 99%

“…LSTMs can be used to recommend films by modelling users' sequential patterns and preferences based on their previous encounters with films. Here's a high-level explanation of how LSTM can be used to recommend films [22][23]:…”

Section: A Lstm For Movie Recommendationmentioning

confidence: 99%